To gather insights on the current and future state of the database ecosystem, we talked to IT executives from 22 companies about how their clients are using databases today and how they see use and solutions changing in the future.

We asked them, "What have I failed to ask that you think we need to cover in this research guide?" Here's what they told us:

People narrowly think about collecting a single time series while customers go through journeys of using the data later, they want to enrich time series with data from other areas (i.e., maintenance data, production data). Adopt a flexible platform allows you to futureproof your database. Traditionally as data infrastructure data has become more siloed to get value from data you must get it out of the silos. Un-silo data and enrich one dataset with another at the query level. When thinking about scalability think about volume. You need to be able to scale across the organization. It's easier to rip and replace than reteach everyone.

Opportunities and challenges to evolve business and the database is part of that. Think about architecture for the next 30 years — agility, nimbleness, survival.

Need to normalize the data. Users want the data now. So relational and non-relational are both important — balance.

Alignment between technology and business will drive better products.

One thing that comes to mind is the challenges and opportunities in interoperability. Data doesn’t just live in a database and show up in a screen: it comes in from multiple systems, and it goes out to other ones. Traditionally, ETL approaches have been the focus for this in the database world, but there is so much more to the picture. In a “Cloud-first, API-first” world, databases and developers that work with them have an opportunity (and a duty) to approach interoperability as part of the application from the start.

The changing roles between developers and DBAs, now SREs (Site Reliability Engineers) replaced the ops person but developers seem to be doing their own work. What’s the evolution of those roles and how does that affect the industry? It started with virtualization and ends with cloud. More DBaaS is changing the role.

Operational stuff is becoming more important (GDPR, build apps so you can operate). The days of DBAs has shifted. You have to think about the data model and its evolution over time. People need to be more of a jack-of-all-trades understanding how it all works. The DevOps model applies to data and databases as well.

AIOps from Gartner is a subset of DataOps.

What role do databases play in the emerging AI landscape? Most data sit in a relational database. The most common source of data is the data lake. All decision trees and neural networks are Graph.

How will AI influence the database?

How machine learning/AI will influence the future of databases.

In the last 30 years, computers have evolved in such ways that they can do a lot more; telephones have evolved in ways that they can do a lot more; cars have evolved in ways that they can do a lot more; search has evolved in ways that they can do a lot more; application development models and applications have evolved to do a lot more; but yet, databases have been nearly stagnant. Why aren’t traditional databases not doing a lot more? Databases need to continue to evolve and do a lot more for the modern developer.